Composite Mapping Research Articles

Underwater Bubble Plume Recognition Algorithm Based on Multi-Feature Fusion Understanding

Underwater bubble plume images contain a wealth of information on wave field and flow characteristics, which can provide valuable research data for marine development, environmental protection, and underwater surveys. However, based on fusing image features and wave field environment features, identifying accurately the underwater bubble plume is still very difficult. In order to improve the accuracy and robustness of bubble plume identification in complex underwater environments, an underwater bubble plume recognition algorithm based on multi-feature fusion understanding is proposed. In this paper, a weight-independent dual-channel residual convolutional neural network (CNN) for feature extraction of the original optical images and the nonsubsampled contourlet transform (NSCT) low-frequency images, and the multi-scale composite feature map groups are generated. Then adaptive fusion is performed based on the feature contribution of the target in different types of images. Next, logical region of interest (ROI) masks are generated by the attention mechanism and superimposed on the fused image to further highlight the target features. Finally, the multi-scale dual-channel fused feature maps containing ROI masks are used for underwater bubble plume target recognition. The experimental results show that the designed recognition network can effectively fuse the features of the original optical images and the NSCT low-frequency imagers, improve the depth of information fusion, and retain the target texture features and the morphological features while reducing the interference of the background information, and have good recognition accuracy and robustness for multi-scale bubble targets in the underwater environment.

QTL mapping for pod quality and yield traits in snap bean (Phaseolus vulgaris L.).

Pod quality and yield traits in snap bean (Phaseolus vulgaris L.) influence consumer preferences, crop adoption by farmers, and the ability of the product to be commercially competitive locally and globally. The objective of the study was to identify the quantitative trait loci (QTL) for pod quality and yield traits in a snap × dry bean recombinant inbred line (RIL) population. A total of 184 F6 RILs derived from a cross between Vanilla (snap bean) and MCM5001 (dry bean) were grown in three field sites in Kenya and one greenhouse environment in Davis, CA, USA. They were genotyped at 5,951 single nucleotide polymorphisms (SNPs), and composite interval mapping was conducted to identify QTL for 16 pod quality and yield traits, including pod wall fiber, pod string, pod size, and harvest metrics. A combined total of 44 QTL were identified in field and greenhouse trials. The QTL for pod quality were identified on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, and Pv07, and for pod yield were identified on Pv08. Co-localization of QTL was observed for pod quality and yield traits. Some identified QTL overlapped with previously mapped QTL for pod quality and yield traits, with several others identified as novel. The identified QTL can be used in future marker-assisted selection in snap bean.

Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples

Objective. Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton range in-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient’s tissue is often needed. Approach. A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample’s carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues. Results. The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous. Significance. Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute to in-vivo dose verification measurements.

Estimation of lithium battery state of charge using the LTG-SABO-GRU model

Abstract Accurate estimation of the state of charge (SOC) in lithium batteries is crucial for optimizing energy utilization and ensuring battery safety within Battery Management Systems (BMS). While deep learning techniques have made significant progress, time-series models based on the Gate Recurrent Unit (GRU) have gained widespread application in SOC estimation. However, their performance heavily hinges on the initial hyperparameter settings, impacting both precision and application range. To address this challenge, we propose a novel algorithm—the Logistic-tent-Gold Subtraction Average-based Optimizer (LTG-SABO)—which combines composite chaotic mapping with the golden sine algorithm. The LTG-SABO algorithm aims to optimize key hyperparameters of the GRU model, thereby enhancing precision and robustness in SOC estimation. By leveraging the Logistic-tent composite chaotic mapping for population initialization, our approach not only expands the search space but also effectively prevents algorithm convergence to local optima. Additionally, integrating the Gold-SA strategy further enhances the global search capability of the SABO algorithm, significantly reducing convergence time. The computational results reveal that the proposed LTG-SABO-GRU model outperforms the traditional GRU model in estimating SOC precision under both normal and extreme temperature conditions. Specifically, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) show a substantial improvement, increasing by over 50% compared to the traditional model. Moreover, the LTG-SABO-GRU model exhibits significantly fewer convergence iterations than existing typical population optimization algorithms. This study introduces a novel, efficient, and practical approach for SOC estimation in BMS applications.

High‐density single nucleotide polymorphism‐based linkage mapping reveals novel quantitative trait loci for rice (Oryza sativa) sheath blight resistance from wild rice (Oryza rufipogon)‐derived introgression line

AbstractRice sheath blight is one of the most important fungal disease‐causing yield losses up to 50%. An Oryza rufipogon‐derived introgression line (Pusa 1908‐13‐12‐5) identified to be highly resistant to sheath blight was used for developing a RIL population by crossing with a highly susceptible rice cultivar, Pusa Basmati 1 (PB 1). The RIL population was genotyped with 80K Rice Pangenome (RPAN) single nucleotide polymorphism (SNP) array and phenotyped for disease response under artificial inoculation for 2 consecutive years. Inclusive composite interval mapping could identify six quantitative trait loci (QTLs) for two traits, namely, relative lesion height and relative yield loss per plant. Among these QTLs, four were major QTLs and three are novel QTLs. Three major QTLs were co‐localised within 1.4 Mb region on chromosome 1. In silico analysis revealed the presence of 12 candidate genes with potential role in disease resistance. Kompetitive Allele Specific PCR (KASP™) assay was developed for the linked SNPs and used for validation in an independent BC1F2:3 population (PB 1121/Pusa 1908‐13‐12‐5). One SNP (AX‐182186537) on chromosome 1 was validated, which offers scope for its utilisation in marker‐assisted breeding.

Genome-Wide Composite Interval Mapping Reveal Closely Linked Quantitative Genes Related to OJIP Test Parameters under Chilling Stress Condition in Barley

Genome-Wide Composite Interval Mapping Reveal Closely Linked Quantitative Genes Related to OJIP Test Parameters under Chilling Stress Condition in Barley

Constructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting

Constructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting

Genetic mapping and QTL analysis of multigenic resistance to powdery mildew (Podosphaera macularis) in hop (Humulus lupulus L.)

AbstractHop powdery mildew (PM) (Podosphaera macularis) causes substantial losses if left uncontrolled. Most resistant hop cultivars possess qualitative resistance based on R‐genes. One cultivar, Comet, has uncharacterized resistance that may be polygenic. This study focused on identifying genomic regions controlling PM resistance in Comet and ascertaining putative genetic mechanisms behind such resistance. A cross between Comet and susceptible male, USDA 64035M, was made. Offspring were screened for resistance under greenhouse conditions and genotyped using genotyping‐by‐sequencing. Genome‐wide analysis using mixed linear model analysis along with quantitative trait locus (QTL) analysis using either composite interval mapping or stepwise regression analyses was performed to identify QTLs. All analyses identified a region on chromosome 6 covering positions 308–314 Mb on the physical map. Analysis of the putative genes within this region identified 140 genes with 27 plant resistance‐like genes found in nine clusters. Six sulfur‐rich protein genes with homology to patatins, thionins, and agglutinins were identified in two clusters. Two glucan‐endo‐1,3‐beta‐glucosidase genes were identified bordering different R‐gene clusters. Finally, putative upregulators of transcription and stress‐response genes were identified. The 10 most highly associated single‐nucleotide polymorphisms for PM resistance were subsequently developed as KASP markers. The combination of R‐gene clusters, sulfur‐rich proteins, endo‐1,3‐beta‐glucosidase genes, and stress‐response genes may be responsible for resistance to PM in the cultivar Comet.

The Aerogeophysical Atlas of Tocantins State: Synthesis of Magnetic Anomaly And Radioelement Maps With Emphasis on Regional Signatures and the Serra Da Cangalha Impact Structure

The Tocantins State Aerogeophysical Atlas (TSAA) is an institutional product of the Geological Survey of Brazil. It is aimed at promoting the use of airborne magnetic and gamma-ray spectrometry data to support geophysical and geological research at state level. Here, we present a synthesis of the TSAA with special emphasis on the interpretation of the magnetic (Anomalous Magnetic Field, First Vertical Derivative (FVD), and Total Gradient Amplitude (TGA) images) and radiometric (RGB composite color map of concentrations, Inverse Images of K, eTh, and eU, and Normalized eU enhancement) map. In particular, the radiometric data for the Serra da Cangalha impact structure in northeast Tocantins are discussed. For Tocantins State, the results show that the radiometric and/or magnetic signatures of geological units (e.g., Mosquito Formation basalts; granite-gneiss/migmatite terrains) and structural features (e.g., dykes and lineaments), or related exogenous deposits (Serra Geral do Tocantins quartzose soils; alluvial deposits of main drainage channels) can be visualized well on regional-scale maps. Analysis of radiometric ratios for the area of the Serra da Cangalha impact structure indicates that eU concentrations are enriched in the annular basin around the central uplift structure, and in the zone straddling the contact between the Piauí and Pedra de Fogo formations. In the latter case, the elevated eU concentrations could be related to brecciated chert layers or silicified sandstones.

Spectroscopic Phenological Characterization of Mangrove Communities

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban.

Identification of quantitative trait loci and candidate genes for grain superoxide dismutase activity in wheat

BackgroundSuperoxide dismutase (SOD) can greatly scavenge reactive oxygen species (ROS) in plants. SOD activity is highly related to plant stress tolerance that can be improved by overexpression of SOD genes. Identification of SOD activity-related loci and potential candidate genes is essential for improvement of grain quality in wheat breeding. However, the loci and candidate genes for relating SOD in wheat grains are largely unknown. In the present study, grain SOD activities of 309 recombinant inbred lines (RILs) derived from the ‘Berkut’ × ‘Worrakatta’ cross were assayed by photoreduction method with nitro-blue tetrazolium (NBT) in four environments. Quantitative trait loci (QTL) of SOD activity were identified using inclusive composite interval mapping (ICIM) with the genotypic data of 50 K single nucleotide polymorphism (SNP) array.ResultsSix QTL for SOD activity were mapped on chromosomes 1BL, 4DS, 5AL (2), and 5DL (2), respectively, explaining 2.2 ~ 7.4% of the phenotypic variances. Moreover, QSOD.xjau-1BL, QSOD.xjau-4DS, QSOD.xjau-5 A.1, QSOD.xjau-5 A.2, and QSOD.xjau-5DL.2 identified are likely to be new loci for SOD activity. Four candidate genes TraesCS4D01G059500, TraesCS5A01G371600, TraesCS5D01G299900, TraesCS5D01G343100LC, were identified for QSOD.xjau-4DS, QSOD.xjau-5AL.1, and QSOD.xjau-5DL.1 (2), respectively, including three SOD genes and a gene associated with SOD activity. Based on genetic effect analysis, this can be used to identify desirable alleles and excellent allele variations in wheat cultivars.ConclusionThese candidate genes are annotated for promoting SOD production and inhibiting the accumulation of ROS during plant growth. Therefore, lines with high SOD activity identified in this study may be preferred for future wheat breeding.

Information-Theoretic Bayesian Inference for Multi-Agent Localization and Tracking of an Radio Frequency Target With Unknown Waveform

Abstract Information-theoretic motion planning and machine learning through Bayesian inference are exploited to localize and track a dynamic radio frequency (RF) emitter with unknown waveform (uncooperative target). A target-state estimator handles non-Gaussian distributions, while mutual information is utilized to coordinate the motion control of a network of mobile sensors (agents) to minimize measurement uncertainty. The mutual information is computed for pairs of sensors through a four-permutation-with-replacement process. The information surfaces are combined to create a composite map, which is then used by agents to plan their motion for more efficient and effective target estimation and tracking. Simulations and physical experiments involving micro-aerial vehicles with time difference of arrival (TDOA) measurements are performed to evaluate the performance of the algorithm. Results show that when two or three agents are used, the algorithm outperforms state-of-the-art methods. Results also show that for four or more agents, the performance is as competitive as an idealized static sensor network.

Compositional and Structural Mapping of Northwest Africa 15507 Angrite

Compositional and Structural Mapping of Northwest Africa 15507 Angrite

High-speed 3D printing temperature system with optimized PlD parameters based on improved ant lion optimization algorithm

An Ant Lion Optimization algorithm based on elite Opposition-based learning and Cosine factors (OCALO) is proposed to address the problem of poor response and stability during heating process in high-speed 3D printing temperature system.The generation of the initial solution in OCALO algorithm is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping, which guarantees the diversity of population. The PID parameters are optimized using the improved algorithm. Compared with two existing classical algorithms and three improved ALO algorithms, the proposed algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The outcomes of simulation and experimentation demonstrate that the algorithm improves the transient and steady-state performance of temperature control with better accuracy and robustness. It takes at least 123 s faster than other controllers to reach stability and is more than two times stronger than other PID controllers, making it better suited to high-speed 3D printing temperature systems.

Determination of the role and factors of technopolis development in the economic security of the depressed regions

Purpose: is to develop the integrated approach to assessing and determining the role and factors of technopolis development in the context of improving economic security of the depressed region.Methods: to analyze the current state of technopolis development in the Russian Federation, the comprehensive multi-component methodological approach was developed, including qualitative and quantitative analysis, as well as statistical methods of data interpretation. This approach was applied to assess the technopolis development of 14 science cities in Russia, the results of which were presented in the form of the composite index heat map. The source of initial data is Rosstat.Results:the study analyzed the evolution of the technopolis concept from the late 19th century to the present. This made it possible to identify the key factors influencing the transformation of scientific and innovation ecosystems. In particular, it was found that modern technopolises in Russia are often the result of renaming or transformation of existing science cities. In the context of economic security of the depressed regions, the factors influencing technopolis development were analyzed. Special attention was paid to the science city of Biysk, located in the largest depressive region of the country – Altai Krai.Conclusions and Relevance: the study showed that technopolises can act as catalysts of economic revival of the depressed regions. However, it also revealed the risks of creating socio-economic imbalances, which indicates the need for a differentiated approach when implementing technopolis projects in the such regions. The results of the study can be used to develop strategies for the management and development of technopolises, as well as to improve the economic security of the depressed regions. The main directions of further research may include analyzing the relationship between technopolis development and economic security, as well as the development of specific management and financing mechanisms.

A combined experimental and simulation study of multiphase bimetallic iodides semiconductor solar cell

The need of high quantum efficiency and stable semiconductor for the photovoltaics are always highly required. In this work, four phase containing multiphase bimetallic iodide (MPBI) compound was synthesized at room temperature by simple and novel chemical route. Synthesized compound was studied for structural, optical, and morphologic properties. The compound structural and phase confirmation was done by XRD, FT-IR, Raman spectroscopy and TEM studies. Optical properties such as absorption and emission characteristics were studied by UV-Visible spectroscopy and fluorescent spectroscopy respectively. The surface morphology and the elemental composition mapping was carried out using SEM and TEM methods. The detailed characterization inferred that high-temperature stable phase Ag4PbI6 was stabilized at room temperature along with AgI, PbI2 and Ag. From the obtained results, the mechanism of Ag4PbI6 phase formation is proposed and discussed. The synthesized semiconductor compound was studied for solar cell applications and obtained efficiency is 5.14 %. Further, the experimental solar cell results were verified using simulation results. This is one of the very few works which reports on room temperature stabilized high temperature stable Ag4PbI6 phase containing multiphase compound.

Detection of closely linked QTLs and candidate genes controlling germination indices in response to drought and salinity stresses in barley

The aim of current study was to identify closely linked QTLs and candidate genes related to germination indices under control, salinity and drought conditions in barley. A total of nine (a major), 28 (eight major) and 34 (five major) closely linked QTLs were mapped on the seven chromosomes in response to control, drought and salinity conditions using genome-wide composite interval mapping, respectively. The major QTLs can be used in marker-assisted selection (MAS) projects to increase tolerance to drought and salinity stresses during the germination. Overall, 422 unique candidate genes were associated with most major QTLs. Moreover, gene ontology analysis showed that candidate genes mostly involved in biological process related to signal transduction and response to stimulus in the pathway of resistance to drought and salinity stresses. Also, the protein–protein interaction network was identified 10 genes. Furthermore, 10 genes were associated with receptor-like kinase family. In addition, 16 transcription factors were detected. Three transcription factors including B3, bHLH, and FAR1 had the most encoding genes. Totally, 60 microRNAs were traced to regulate the target genes. Finally, the key genes are a suitable and reliable source for future studies to improve resistance to abiotic stress during the germination of barley.

Different negations and fuzziness in Web resources and resource description and their mathematical foundation

In Web resources and resource descriptions, many resources and descriptions of resources have different negations and fuzziness. In this paper, we investigate and analyze the cognition and characteristics of “negation” in philosophy, logic, linguistics and other knowledge fields, and based on the distinction between the concept of “contradiction” and the concept of “opposition”, and “clear concept” and “fuzzy concept”, we propose that there are three different kinds of negation in knowledge from the conceptual level, namely “contradictory negation”, “opposite negation” and “intermediary negation”. In order to establish a mathematical basis that can fully reflect the internal relations, properties and laws of these different negations and fuzziness, we propose a “Scoi: sets with contradictory negation, opposite negation and intermediary negation” and “Lcoi&PLcoi: logic with contradictory negation, opposite negation and intermediary negation”, and the operational properties of Scoi and the formal deduction properties of Lcoi have been proven. The soundness theorem, completeness theorem and compactness theorem for Lcoi are proved under the three-valued semantic interpretation of Lcoi, and the soundness and completeness for Lcoi&PLcoi are discussed under the given an infinite-valued semantics interpretation of Lcoi&PLcoi. Furthermore, we also demonstrated that descriptions of objective things such as “x is neither a positive nor a negative integer” and “he is neither rich nor poor” typle of statements are “intermediary negation” in Scoi and Lcoi&PLcoi. It shows that “intermediary negation” in Scoi and Lcoi&PLcoi has objectively.In order to enable RDF(Resource Description Framework) to describe Web resources with different negations and fuzziness, we extend RDF based on Scoi and Lcoi&PLcoi, and propose a resource description framework RDFcoi which can fully handle fuzzy attribute values and their different negations in attributes of resources. How to reflect the uncertainty of the relationship between Web resources and its attribute values, we believe that the relationship between a resource and fuzzy attribute value in one of its attributes should be a composite mapping from the resource domain to the fuzzy attribute value set and then to [0, 1], and a mathematical representation is given in this paper. Thus, RDFcoi differ from other RDF extensions. RDFcoi maintains the basic syntax and normative of RDF, and extends the basic syntax and semantics of RDF. Where, the concepts of RDFcoi triple with three kinds of negation, RDFcoi formula and RDFcoi graph are defined. RDFcoi vocabulary is determined and an interpretation is given. RDFcoi-entailment is defined and its properties are discussed. In addition, we also study the reasoning regimes of RDFcoi. By using the given logical translation method, a well-typed and interoperable RDFcoi graph can be transformed into a set of formulas in Lcoi&PLcoi, thus establishing “interoperability” between RDFcoi and Lcoi&PLcoi. From the perspective of reasoning mechanism of RDFcoi-entailment, RDFcoi reasoning can be carried out in Lcoi&PLcoi, and RDFcoi has the same reasoning mechanism as formal inference in Lcoi&PLcoi.

Quantitative Trait Mapping for Zebra-stem in Tomato Confirms a Genetic Cause Involving the Interaction of Unlinked Loci

Zebra-stem of tomato is a disorder characterized by leaf necrosis, wilting, and a stripped pattern on stems of mature plants. Wilting, necrosis, and death of seedlings are also observed. The physiological and genetic causes of zebra-stem are poorly characterized. Anecdotal evidence has suggested pedigrees with S. pimpinellifolium and bacterial speck resistance in the genetic background are often prone to this disorder. We demonstrate a genetic cause using composite interval mapping and association analysis approaches to define quantitative trait loci (QTLs) that contribute to the disorder. A biparental population of F4 partially inbred families was developed for the initial analysis, and four subsequent backcross or F2 populations were used for subsequent validation. Significant QTLs on chromosomes 5 and 10 were identified, explaining ∼60% and 40% of the variation, respectively. Polymorphisms in the Pto locus are strongly associated with the QTL on chromosome 5. The two loci were derived from different parentage, and a significant interaction effect was demonstrated, resulting in the characteristic zebra-stem symptoms when combined.

Dissecting the shared genetic architecture between anti-Müllerian hormone and age at menopause based on genome-wide association study

BackgroundWhile the phenotypic association between anti-Müllerian hormone (AMH) and age at menopause has been widely studied, the role of AMH in predicting the age at menopause is currently controversial, and the genetic architecture or causal relationships underlying these two traits is not well understood. AimWe aimed to explore the shared genetic architecture between AMH and age at menopause, to identify shared pleiotropic loci and genes, and to investigate causal association and potential causal mediators. Study DesignUsing summary statistics from publicly available genome-wide association studies on AMH (N=7,049) and age at menopause (N=201,323) in Europeans, we investigated the global genetic architecture between AMH and age at menopause through linkage disequilibrium score regression. We employed pleiotropic analysis under composite null hypothesis (PLACO), Functional Mapping and Annotation of Genetic Associations (FUMA), Multimarker analysis of GenoMic annotation (MAGMA), and colocalization analysis to identify loci and genes with pleiotropic effects. Tissue enrichment analysis based on GTEx data was conducted using the Linkage Disequilibrium Score for the specific expression of genes analysis (LDSC-SEG). Functional genes that were shared were additionally identified through summary data-based Mendelian randomization (SMR). The relationship between AMH and age at menopause was examined through two-sample Mendelian randomization (MR), and potential mediators were further explored using colocalization and metabolite-mediated analysis. ResultsA positive genetic association (correlation coefficient = 0.88, P = 1.33 × 10-5) was observed between AMH and age at menopause. By using PLACO and FUMA, 42 significant pleiotropic loci were identified that were associated with AMH and age at menopause, and ten of these (rs10734411, rs61913600, rs2277339, rs75770066, rs28416520, rs9796, rs11668344, rs403727, rs6011452, and rs62237617) had colocalized loci. Additionally, 245 significant pleiotropic genes were identified by MAGMA. Genetic associations between AMH and age at menopause were markedly concentrated in various tissues including whole blood, brain, heart, liver, muscle, pancreas, and kidneys. Further, SMR analysis revealed nine genes that may have a causative effect on both AMH and age at menopause. A potential causal effect of age at menopause on AMH was suggested by two-sample MR analysis, with very-low-density lipoprotein identified as a potential mediator. ConclusionsOur study revealed a shared genetic architecture between AMH and age at menopause, providing a basis for experimental investigations and individual therapies to enhance reproductive outcomes. Furthermore, our findings emphasized that relying solely on AMH is not sufficient for accurately predicting the age at menopause, and a combination of other factors needs to be considered. Exploring new therapeutics aimed at delaying at the onset of menopause holds promise, particularly when targeting shared genes based on their shared genetic architecture.